PřF:Bi6446 Time Series Forecasting - Course Information

Bi6446 Time Series Forecasting

Extent and Intensity

2/1/0. 3 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).

Teacher(s)

prof. Ing. Jiří Holčík, CSc. (lecturer)

Guaranteed by

prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: prof. Ing. Jiří Holčík, CSc.
Supplier department: RECETOX – Faculty of Science

Prerequisites (in Czech)

Bi5440 Time series

Course Enrolment Limitations

The course is also offered to the students of the fields other than those the course is directly associated with.

fields of study / plans the course is directly associated with

• Epidemiology and modeling (programme PřF, N-MBB)
• Mathematical Biology (programme PřF, N-EXB)
• Modelling and Calculations (programme PřF, B-MA)

Course objectives

At the end of the course, students should be able to:
- know fundamental theoretical and methodological principles of methods of time series prediction not only with emphasis to biological data processing
- explain consequences and relationships between characteristics of real processes and data and applied methods and algorithms;
- apply different practical approaches to data processing to obtain required analytic results;
- design modified algorithms to process data of given particular characteristics

Learning outcomes

At the end of the course, students should be able to:
- know fundamental theoretical and methodological principles of methods of time series spectral analysis with emphasis to biological data processing
- explain consequences and relationships between characteristics of real processes and data and applied methods and algorithms;
- apply different practical approaches to data processing to obtain required analytic results;
- design modified algorithms to process data of given particular characteristics.

Syllabus

• 1. Why prediction usually fails.
• 2. Prediction – what is it?, preliminary analysis, transformation & adjustments, prediction models - method of simple forecasting.
• 3. Prediction models – regression, linear prediction (autoregressive models, moving average models).
• 4. Prediction models – linear prediction (exponential smoothing).
• 5. Judgemental forecasting.
• 6. Forecasting evaluation

Teaching methods

Lectures supported by Power Point presentations. Understanding of principles, methods and algorithms is emphasized. Students are continuously encouraged to be in an interaction with a lecturer.

Assessment methods

oral examination

Language of instruction

Czech

Further comments (probably available only in Czech)

The course is taught annually.
General note: Vhodné je mít základy metod zpracování signálů a spektrální analýzy.

• Enrolment Statistics (Spring 2020, recent)
  
• Permalink: https://is.muni.cz/course/sci/spring2020/Bi6446